Lead Backend Engineer

This role combines hands-on engineering with technical leadership, offering the opportunity to guide architectural decisions, solve complex engineering challenges, mentor developers, and drive successful delivery across the full software development lifecycle. The ideal candidate brings deep expertise in big data technologies, AWS cloud infrastructure, and performance optimization, along with a demonstrated ability to lead engineering teams and remove technical impediments.

Bachelor’s degree in Computer Science, Engineering, or a related technical field. \n

• \n 10+ years of professional software engineering experience with demonstrated technical leadership. \n

• \n 5+ years of hands-on experience with big data technologies such as Hadoop, Spark, or Presto, including deep understanding of system internals, performance tuning, and optimization. \n

• \n Proven experience with AWS cloud services, including EMR (Hive/Presto), ECS, Lambda, and S3, supporting large-scale applications. \n

• \n 3+ years of hands-on development experience with Java, JavaScript/TypeScript, or SQL. \n

• \n 2+ years of experience designing and developing RESTful APIs or web services., The successful candidate is a hands-on technical leader who combines deep expertise in big data engineering and AWS with strong project execution and team mentorship capabilities. They are comfortable solving complex performance challenges, guiding architectural decisions, removing delivery obstacles, and helping engineers produce high-quality solutions.

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They bring a collaborative leadership style, strong technical judgment, and a forward-thinking approach to incorporating AI-assisted development practices into modern software engineering workflows.

Key ResponsibilitiesBig Data Engineering & Technical Leadership\n \n

• \n Lead the design, development, and delivery of scalable solutions using distributed microservices and big data technologies such as Hive, Presto, Spark, and Hadoop. \n

• \n Provide technical direction for AWS-based applications and infrastructure. \n

• \n Serve as a technical expert in big data performance, diagnosing bottlenecks and guiding engineers through complex system-level challenges. \n

• \n Drive architectural decisions that improve system scalability, reliability, and performance. \n

Project Leadership & Delivery\n \n

• \n Own end-to-end technical project delivery, ensuring solutions meet business requirements and engineering standards. \n

• \n Proactively identify and resolve technical, process-related, and cross-functional impediments. \n

• \n Translate business requirements into technical roadmaps, actionable development tasks, and implementation strategies. \n

• \n Collaborate with stakeholders to establish priorities, manage dependencies, and ensure successful delivery. \n

Team Mentorship & Engineering Excellence\n \n

• \n Mentor engineers through code reviews, technical guidance, and constructive feedback. \n

• \n Establish and promote engineering best practices related to code quality, performance, maintainability, and testing. \n

• \n Partner with product management, architecture, QA, and DevOps teams to deliver scalable, reliable solutions. \n

• \n Foster a collaborative engineering environment focused on continuous improvement and technical excellence. \n

AI-Assisted Software Development\n \n

• \n Champion the adoption of AI-powered development tools, such as Kiro, GitHub Copilot, or similar technologies. \n

• \n Guide the team in leveraging AI-assisted capabilities throughout the software development lifecycle, including design, coding, testing, deployment, and production support. \n

• \n Establish standards for responsible AI-assisted development, ensuring generated code is understood, reviewed, tested, and validated before implementation. \n, * \n Demonstrated experience leading end-to-end technical project delivery and resolving engineering impediments. \n

• \n Experience mentoring software engineers, conducting code reviews, and establishing engineering best practices. \n

• \n Proficiency with CI/CD pipelines and DevOps tools such as Jenkins and CloudFormation. \n

• \n Working knowledge of scripting languages such as Python or Unix Shell. \n

• \n Hands-on experience using LLM-based coding assistants, such as Kiro, GitHub Copilot, or similar tools, across the software development lifecycle. \n

• \n Ability to guide engineering teams in the responsible use of AI-assisted development tools, including validation of AI-generated code. \n

Preferred Qualifications (Nice-to-Have)\n \n

• \n Experience designing or modernizing large-scale distributed systems and cloud-native architectures. \n

• \n Experience optimizing high-performance APIs and data-intensive applications for low-latency access. \n

• \n Familiarity with AWS Well-Architected Framework principles and cloud performance optimization. \n

• \n Experience leading greenfield development initiatives or major platform modernization efforts. \n

• \n Exposure to emerging AI/ML technologies and their application to software engineering. \n

• \n Experience coordinating technical delivery across multiple engineering teams and stakeholders. \n

• \n Strong understanding of Agile methodologies, technical roadmapping, and delivery planning. \n

• \n Demonstrated ability to influence architectural decisions and drive engineering improvements across teams. \n

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